
"""simple docstring"""



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    return getitem, k


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	str:





    """simple docstring"""

    return setitem, k, v


def 			_UpperCamelCase						(    _A   )     ->	Dict:





    """simple docstring"""

    return delitem, k


def 			_UpperCamelCase						(    _A       ,   _A       ,   *_A   )     ->	int:





    """simple docstring"""

    try:
        return fun(_A       ,   *_A   ), None
    except Exception as e:
        return None, e


a       :						List[Any]       		=		(
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

a       :						Optional[int]       		=		[
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

a       :						List[Any]       		=		[
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

a       :						int       		=		[
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

a       :						Optional[int]       		=		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a       :						str       		=		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('''key_a''', '''val_b'''),
]


@pytest.mark.parametrize(
    """operations"""       ,   (
        pytest.param(_add_items       ,   id="""add items"""   ),
        pytest.param(_overwrite_items       ,   id="""overwrite items"""   ),
        pytest.param(_delete_items       ,   id="""delete items"""   ),
        pytest.param(_access_absent_items       ,   id="""access absent items"""   ),
        pytest.param(_add_with_resize_up       ,   id="""add with resize up"""   ),
        pytest.param(_add_with_resize_down       ,   id="""add with resize down"""   ),
    )       ,   )
def 			_UpperCamelCase						(    _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = HashMap(initial_block_size=4   )
    _UpperCAmelCase        = {}
    for _, (fun, *args) in enumerate(_A   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = _run_operation(_A       ,   _A       ,   *_A   )
        _UpperCAmelCase     ,_UpperCAmelCase        = _run_operation(_A       ,   _A       ,   *_A   )
        assert my_res == py_res
        assert str(_A   ) == str(_A   )
        assert set(_A   ) == set(_A   )
        assert len(_A   ) == len(_A   )
        assert set(my.items()   ) == set(py.items()   )







def 			_UpperCamelCase						(    )     ->	int:





    """simple docstring"""

    def is_public(_A   ) -> bool:
        return not name.startswith("""_"""   )

    _UpperCAmelCase        = {name for name in dir({}   ) if is_public(_A   )}
    _UpperCAmelCase        = {name for name in dir(HashMap()   ) if is_public(_A   )}

    assert dict_public_names > hash_public_names

"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step